Adventures in Machine Learning

Mastering Techniques for Updating Python Dataframe Rows

Updating Row Values in a Python Dataframe

Dataframes are a fundamental data structure in the Pandas library that make it easy to work with tabular data. They are similar to a spreadsheet, with rows and columns that can be manipulated and analyzed in various ways using Python.

In this article, we will explore various methods of updating row values in a Python Dataframe.

Creating a Dataframe

Before we can update values in a Dataframe, we need to first create one. We can do this using the pd.DataFrame() function, passing in a list or an array of data.

For example, let’s create a Dataframe of student grades.

import pandas as pd
data = {'Name': ['John', 'Lisa', 'Mike', 'Sarah'],
        'Math': [85, 95, 72, 90],
        'Science': [93, 88, 65, 80],
        'English': [88, 91, 70, 85]}
df = pd.DataFrame(data)
print(df)

Output:

    Name  Math  Science  English
0   John    85       93       88
1   Lisa    95       88       91
2   Mike    72       65       70
3  Sarah    90       80       85

Using Python at() method to update the value of a row

The at() method in Pandas is used to access a single value in a Dataframe. We can use this method to update a single value in a row.

For example, let’s change Sarah’s Math grade to 95.

df.at[3, 'Math'] = 95
print(df)

Output:

    Name  Math  Science  English
0   John    85       93       88
1   Lisa    95       88       91
2   Mike    72       65       70
3  Sarah    95       80       85

Python loc() function to change the value of a row/column

The loc() function is more versatile than the at() method when it comes to accessing and modifying values in a Dataframe. We can use this function to update multiple values in a row or column at once.

For example, let’s update all of Mike’s grades to 80.

df.loc[df['Name'] == 'Mike', ['Math', 'Science', 'English']] = 80
print(df)

Output:

    Name  Math  Science  English
0   John    85       93       88
1   Lisa    95       88       91
2   Mike    80       80       80
3  Sarah    95       80       85

Python replace() method to update values in a dataframe

The replace() method can be used to replace specific values in a Dataframe with new values. For example, let’s replace all instances of 80 with 85 in the Math column.

df['Math'] = df['Math'].replace(80, 85)
print(df)

Output:

    Name  Math  Science  English
0   John    85       93       88
1   Lisa    95       88       91
2   Mike    85       80       80
3  Sarah    95       80       85

Using iloc() method to update the value of a row

Finally, we can use the iloc() method to update a row by its integer index. For example, let’s update Lisa’s grades to 90 in one line of code.

df.iloc[1, 1:] = 90
print(df)

Output:

    Name  Math  Science  English
0   John    85       93       88
1   Lisa    90       90       90
2   Mike    85       80       80
3  Sarah    95       80       85

Conclusion

In this article, we have explored various techniques for updating row values in a Python Dataframe. We have demonstrated how to use the at(), loc(), and iloc() methods to modify specific values or ranges of values in a DataFrame, as well as how to use the replace() method to change all instances of a particular value.

By mastering these techniques, you will be able to efficiently manipulate and analyze your tabular data in Python with ease. To update row values in a Python Dataframe, there are various techniques available such as at(), loc(), iloc(), and replace() methods.

We can use the at() method to modify a single value in a row while loc() is used to modify multiple values in a row or column. The replace() method is used to change all instances of a particular value.

Finally, we can use the iloc() method to update a row using its integer index. With these techniques, we can efficiently manipulate and analyze our tabular data in Python with ease, emphasizing the importance of the topic.

Popular Posts